Masashi Sugiyama

research

∙ 09/15/2023

Unified Risk Analysis for Weakly Supervised Learning

Among the flourishing research of weakly supervised learning (WSL), we r...

0 Chao-Kai Chiang, et al. ∙

research

∙ 08/20/2023

Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit

We study the real-valued combinatorial pure exploration of the multi-arm...

0 Shintaro Nakamura, et al. ∙

research

∙ 08/12/2023

Multi-Label Knowledge Distillation

Existing knowledge distillation methods typically work by imparting the ...

0 Penghui Yang, et al. ∙

research

∙ 07/21/2023

Distribution Shift Matters for Knowledge Distillation with Webly Collected Images

Knowledge distillation aims to learn a lightweight student network from ...

0 Jialiang Tang, et al. ∙

research

∙ 07/12/2023

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation

Generating unlabeled data has been recently shown to help address the fe...

0 Ruijiang Dong, et al. ∙

research

∙ 06/15/2023

Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class

The combinatorial pure exploration (CPE) in the stochastic multi-armed b...

0 Shintaro Nakamura, et al. ∙

research

∙ 06/12/2023

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision

Training a classifier exploiting a huge amount of supervised data is exp...

0 Yuhao Wu, et al. ∙

research

∙ 05/28/2023

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning

Label-noise learning (LNL) aims to increase the model's generalization g...

1 Jingfeng Zhang, et al. ∙

research

∙ 05/24/2023

Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems

Distribution shift (DS) may have two levels: the distribution itself cha...

0 Tongtong Fang, et al. ∙

research

∙ 05/19/2023

Enriching Disentanglement: Definitions to Metrics

Disentangled representation learning is a challenging task that involves...

0 Yivan Zhang, et al. ∙

research

∙ 05/16/2023

Analysis of Pleasantness Evoked by Various Airborne Ultrasound Tactile Stimuli Using Pairwise Comparisons and the Bradley-Terry Model

The presentation of a moving tactile stimulus to a person's forearm evok...

0 Sora Satake, et al. ∙

research

∙ 05/15/2023

Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation

Complementary-label Learning (CLL) is a form of weakly supervised learni...

0 Wei-I Lin, et al. ∙

research

∙ 05/11/2023

A Category-theoretical Meta-analysis of Definitions of Disentanglement

Disentangling the factors of variation in data is a fundamental concept ...

0 Yivan Zhang, et al. ∙

research

∙ 05/04/2023

Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning

Pseudo labeling is a popular and effective method to leverage the inform...

0 Ming-Kun Xie, et al. ∙

research

∙ 04/30/2023

Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks

Adversarial training (AT) is a robust learning algorithm that can defend...

1 Jingfeng Zhang, et al. ∙

research

∙ 04/30/2023

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization

Adversarial contrastive learning (ACL), without requiring labels, incorp...

3 Xilie Xu, et al. ∙

research

∙ 02/28/2023

Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits

Thompson sampling (TS) for the parametric stochastic multi-armed bandits...

0 Jongyeong Lee, et al. ∙

research

∙ 02/08/2023

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection

Adversarial contrastive learning (ACL) does not require expensive data a...

1 Xilie Xu, et al. ∙

research

∙ 02/06/2023

GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks

While leveraging additional training data is well established to improve...

1 Salah Ghamizi, et al. ∙

research

∙ 02/06/2023

Adapting to Continuous Covariate Shift via Online Density Ratio Estimation

Dealing with distribution shifts is one of the central challenges for mo...

0 Yu-Jie Zhang, et al. ∙

research

∙ 02/03/2023

Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits

In the stochastic multi-armed bandit problem, a randomized probability m...

0 Jongyeong Lee, et al. ∙

research

∙ 12/26/2022

Robust computation of optimal transport by β-potential regularization

Optimal transport (OT) has become a widely used tool in the machine lear...

0 Shintaro Nakamura, et al. ∙

research

∙ 11/23/2022

Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning

Deep reinforcement learning (DRL) breaks through the bottlenecks of trad...

0 Tingting Zhao, et al. ∙

research

∙ 11/01/2022

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks

Adversarial training (AT) with imperfect supervision is significant but ...

1 Jianan Zhou, et al. ∙

research

∙ 10/27/2022

Audio Signal Enhancement with Learning from Positive and Unlabelled Data

Supervised learning is a mainstream approach to audio signal enhancement...

0 Nobutaka Ito, et al. ∙

research

∙ 08/03/2022

Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

Machine learning systems may encounter unexpected problems when the data...

0 Yivan Zhang, et al. ∙

research

∙ 07/05/2022

Adapting to Online Label Shift with Provable Guarantees

The standard supervised learning paradigm works effectively when trainin...

3 Yong Bai, et al. ∙

research

∙ 07/04/2022

Learning from Multiple Unlabeled Datasets with Partial Risk Regularization

Recent years have witnessed a great success of supervised deep learning,...

0 Yuting Tang, et al. ∙

research

∙ 06/07/2022

The Survival Bandit Problem

We study the survival bandit problem, a variant of the multi-armed bandi...

0 Charles Riou, et al. ∙

research

∙ 06/06/2022

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation

In label-noise learning, estimating the transition matrix has attracted ...

0 De Cheng, et al. ∙

research

∙ 06/02/2022

Excess risk analysis for epistemic uncertainty with application to variational inference

We analyze the epistemic uncertainty (EU) of supervised learning in Baye...

0 Futoshi Futami, et al. ∙

research

∙ 04/15/2022

Universal approximation property of invertible neural networks

Invertible neural networks (INNs) are neural network architectures with ...

0 Isao Ishikawa, et al. ∙

research

∙ 04/07/2022

Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

Supervised federated learning (FL) enables multiple clients to share the...

25 Nan Lu, et al. ∙

research

∙ 02/22/2022

On the Effectiveness of Adversarial Training against Backdoor Attacks

DNNs' demand for massive data forces practitioners to collect data from ...

1 Yinghua Gao, et al. ∙

research

∙ 02/07/2022

Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests

Non-parametric two-sample tests (TSTs) that judge whether two sets of sa...

1 Xilie Xu, et al. ∙

research

∙ 02/01/2022

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification

There is a fundamental limitation in the prediction performance that a m...

0 Takashi Ishida, et al. ∙

research

∙ 01/12/2022

Towards Adversarially Robust Deep Image Denoising

This work systematically investigates the adversarial robustness of deep...

10 Hanshu Yan, et al. ∙

research

∙ 12/23/2021

Learning with Proper Partial Labels

Partial-label learning is a kind of weakly-supervised learning with inex...

0 Zhenguo Wu, et al. ∙

research

∙ 12/19/2021

Rethinking Importance Weighting for Transfer Learning

A key assumption in supervised learning is that training and test data f...

0 Nan Lu, et al. ∙

research

∙ 09/29/2021

Active Refinement for Multi-Label Learning: A Pseudo-Label Approach

The goal of multi-label learning (MLL) is to associate a given instance ...

0 Cheng-Yu Hsieh, et al. ∙

research

∙ 07/16/2021

Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences

Ordinary supervised learning is useful when we have paired training data...

0 Ikko Yamane, et al. ∙

research

∙ 07/11/2021

Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation

Learning from positive and unlabeled (PU) data is an important problem i...

0 Shota Nakajima, et al. ∙

research

∙ 06/17/2021

Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations

In many real-world imitation learning tasks, the demonstrator and the le...

3 Xin-Qiang Cai, et al. ∙

research

∙ 06/16/2021

Multi-Class Classification from Single-Class Data with Confidences

Can we learn a multi-class classifier from only data of a single class? ...

0 Yuzhou Cao, et al. ∙

research

∙ 06/15/2021

Probabilistic Margins for Instance Reweighting in Adversarial Training

Reweighting adversarial data during training has been recently shown to ...

0 Qizhou Wang, et al. ∙

research

∙ 06/11/2021

On the Robustness of Average Losses for Partial-Label Learning

Partial-label (PL) learning is a typical weakly supervised classificatio...

0 Jiaqi Lv, et al. ∙

research

∙ 06/09/2021

Loss function based second-order Jensen inequality and its application to particle variational inference

Bayesian model averaging, obtained as the expectation of a likelihood fu...

0 Futoshi Futami, et al. ∙

research

∙ 06/01/2021

Instance Correction for Learning with Open-set Noisy Labels

The problem of open-set noisy labels denotes that part of training data ...

0 Xiaobo Xia, et al. ∙

research

∙ 06/01/2021

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

In learning with noisy labels, the sample selection approach is very pop...

0 Xiaobo Xia, et al. ∙

research

∙ 05/31/2021

A unified view of likelihood ratio and reparameterization gradients

Reparameterization (RP) and likelihood ratio (LR) gradient estimators ar...

0 Paavo Parmas, et al. ∙

Masashi Sugiyama

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